Machine Learning for Developers

9.

Production Monitoring and Maintenance

9.1.

Performance Monitoring

9.1.1.

Model Performance Metrics

9.1.1.1.

Accuracy Tracking

9.1.1.2.

Latency Monitoring

9.1.1.3.

Throughput Measurement

9.1.2.

System Performance Metrics

9.1.2.1.

Resource Utilization

9.1.2.2.

9.1.2.3.

Availability Metrics

9.1.3.

Business Metrics

9.1.3.1.

9.1.3.2.

ROI Measurement

9.1.3.3.

User Satisfaction

9.2.

Data and Model Drift Detection

9.2.1.

9.2.1.1.

Statistical Tests

9.2.1.2.

Distribution Comparison

9.2.1.3.

Feature Drift Detection

9.2.2.

9.2.2.1.

9.2.2.2.

Detection Algorithms

9.2.2.3.

Adaptation Strategies

9.2.3.

9.2.3.1.

Performance Degradation

9.2.3.2.

Prediction Drift

9.2.3.3.

Confidence Drift

9.3.

Model Maintenance Strategies

9.3.1.

Retraining Triggers

9.3.1.1.

Performance Thresholds

9.3.1.2.

Time-Based Triggers

9.3.1.3.

Data Volume Triggers

9.3.2.

Retraining Approaches

9.3.2.1.

Full Retraining

9.3.2.2.

Incremental Learning

9.3.2.3.

Online Learning

9.3.3.

Model Rollback Strategies

9.3.3.1.

Version Management

9.3.3.2.

Rollback Triggers

9.3.3.3.

Recovery Procedures

9.4.

Logging and Alerting

9.4.1.

Prediction Logging

9.4.1.1.

9.4.1.2.

9.4.1.3.

Metadata Logging

9.4.2.

9.4.2.1.

Exception Handling

9.4.2.2.

Error Classification

9.4.2.3.

Root Cause Analysis

9.4.3.

Alert Configuration

9.4.3.1.

Threshold-Based Alerts

9.4.3.2.

Anomaly-Based Alerts

9.4.3.3.

Escalation Procedures

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8. Model Deployment and MLOps

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10. Natural Language Processing for Developers